99.9% CPU and High memory

Hello guys! today I just saw that my VPS shows this title data: CPU 99.9% and memory High
Why is this being generated?
Previously everything was perfect! I had all GSA INDEX, 5 campaigns, GSA CB and everything is fine until today. Suddenly it started to generate a high CPU.

⚡⚡30 GB Raid 10 Storage⚡⚡ OpenVZ VPS – 1024 MB Memory – Free cPanel – Free Backups –

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Why is MEMORYCLERK_SQLGENERAL using so much memory in SQL Server?

I have a production system running SQL Server 2019 Standard edition. It recently had a problem 3 times in 1 day where it became unresponsive until a reboot. Errors seem to point to memory limitations. 32GB is installed and the machine is dedicated to MSSQL. Max Memory set to 26GB.

The best lead I have so far is output of dbcc memorystatus that was automatically dumped to the log along with a FAIL_PAGE_ALLOCATION error. The full output is attached, but this part below caught my eye. It looks like MEMORYCLERK_SQLGENERAL wanted so much memory that it forced normal things like the buffer pool and query memory down to uselessly small levels.

I can’t seem to find any good info on what MEMORYCLERK_SQLGENERAL does, let alone why it would want so much memory.

11/18/2020 15:10:48,spid51,Unknown,MEMORYCLERK_SQLGENERAL (node 0)                  KB Pages Allocated                            22821672 SM Committed                                      0 SM Reserved                                       0 Locked Pages Allocated                       546740 VM Committed                                  75776 VM Reserved                                12867644 ---------------------------------------- ---------- 11/18/2020 15:10:48,spid51,Unknown,MEMORYCLERK_SQLBUFFERPOOL (node 0)               KB Pages Allocated                                3400 SM Committed                                      0 SM Reserved                                       0 Locked Pages Allocated                            0 VM Committed                                      0 VM Reserved                                       0 ---------------------------------------- ---------- 11/18/2020 15:10:48,spid51,Unknown,MEMORYCLERK_SQLQUERYPLAN (node 0)                KB Pages Allocated                                3632 SM Committed                                      0 SM Reserved                                       0 Locked Pages Allocated                            0 VM Committed                                      0 VM Reserved                                       0 ---------------------------------------- ---------- 11/18/2020 15:10:48,spid51,Unknown,MEMORYCLERK_SQLQUERYEXEC (node 0)                KB Pages Allocated                                1128 SM Committed                                      0 SM Reserved                                       0 Locked Pages Allocated                            0 VM Committed                                      0 VM Reserved                                       0 

Should I write custom allocators for STL containers to interface with my memory pool, or just overwrite the standard new and delete

I want to write a custom memory allocator for learning. I’m tempted to have a master allocator that requests n bytes of ram from the heap (via new). This would be followed by several allocator… Adaptors? Each would interface with the master, requesting a block of memory to manage, these would be stack, linear, pool, slab allocators etc.

The problem I have is whether I should write custom allocator_traits to interface with these for the various STL containers; or if I should just ignore the adaptor idea and simply overload new and delete to use a custom pool allocator.

What I’m interested in understanding is what tangible benefit I would gain from having separate allocators for STL containers? It seems like the default std::allocator calls new and delete as needed so if I overload those to instead request from my big custom memory pool, I’d get all the benefit without the kruft of custom std::allocator code.

Or is this a matter where certain types of allocator models, like using a stack allocator for a std::dqueue would work better than the default allocator? And if so, wouldn’t the normal stl implementation already specialise?

A 32 – bit wide main memory unit with a capacity of 1 GB is built using 256M X 4-bit DRAM chips

A 32 – bit wide main memory unit with a capacity of 1 GB is built using 256M X 4-bit DRAM chips. The number of rows of memory cells in the DRAM chip is 2^14. The time taken to perform one refresh operation is 50 nanoseconds. The refresh period is 2 milliseconds. The percentage (rounded to the closet integer) of the time available for performing the memory read/write operations in the main memory unit is _______ .

I calculated that the no of DRAM chips needed is 32. Now each DRAM have rows = 2^14
and columns 2^16 also as we can refresh the rows in parallel and since for one memory cell the time is 50 nanoseconds so for 2^16 columns we will need 2^16 * 50 nano sec ?…Is my approach right

or if i consider that 50 nanoseconds is the time for refresh of a complete row then also it would need a total of 50 nanosec to refresh all in parallel

memory storage of a program before compiling

Whenever we write code, after compilation the code will be converted to machine language and then stored in the hard disk. But before compiling the code, it is still in the high-level language. How and where the memory is allocated for the code before compiling the code while it is in a high-level language.

I assume, before compiling the code is stored in RAM, but how? because we can only store in machine language in RAM.

If there is any wrong with my question or it is a wrong way of asking, please comment below. It will be helpful